1 code implementation • 1 Feb 2021 • Nihal Rao, Ke Liu, Marc Machaczek, Lode Pollet
We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine (TK-SVM), to investigate the low-temperature classical phase diagram of a generalized Heisenberg-Kitaev-$\Gamma$ ($J$-$K$-$\Gamma$) model on a honeycomb lattice.
1 code implementation • 29 Apr 2020 • Ke Liu, Nicolas Sadoune, Nihal Rao, Jonas Greitemann, Lode Pollet
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases.
2 code implementations • 29 Jul 2019 • Jonas Greitemann, Ke Liu, Ludovic D. C. Jaubert, Han Yan, Nic Shannon, Lode Pollet
Machine-learning techniques have proved successful in identifying ordered phases of matter.
Strongly Correlated Electrons Statistical Mechanics
2 code implementations • 11 Oct 2018 • Ke Liu, Jonas Greitemann, Lode Pollet
Furthermore, we discuss an intrinsic parameter of SVM, the bias, which allows for a special interpretation in the classification of phases, and its utility in diagnosing the existence of phase transitions.
Statistical Mechanics Strongly Correlated Electrons Computational Physics
2 code implementations • 23 Apr 2018 • Jonas Greitemann, Ke Liu, Lode Pollet
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism.